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Classiﬁcation of divorce causes during the COVID-19 pandemic using convolutional neural networks

PeerJ Computer Science 2022年 8卷

作者： Bramantoro, Arif Virdyna, Inge School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia

The COVID-19 pandemic has affected day-to-day activities. Some families experienced a positive impact, such as an increase of bonding between family members. However, there are families that experienced a negative effect, such as the emergence of various conﬂicts that lead to a divorce. Based on the literature, it can be stated that the COVID-19 pandemic contributed to the increasing number of divorce rates. This paper proposes a convolutional neural network (CNN) classiﬁcation algorithm in determining the dominant causes of the increase in divorce rate during the COVID-19 pandemic. CNN is considered suitable for classifying large amounts of data. The data used as research materials are available on the ofﬁcial website of the Indonesian Supreme Court. This research utilizes Supreme Court divorce decisions from March 2020 to July 2021, which constitutes 15,997 datasets. The proposed number of layers implemented during the classiﬁcation is four. The results indicate that the classiﬁcation using CNN is able to provide an accuracy value of 96% at the 100th epoch. To provide a baseline comparison, the classical support vector machine (SVM) method was performed. The result conﬁrms that CNN outweighs SVM. It is expected that the results will help any parties to provide a suitable anticipation based on the classiﬁed dominant causes of the divorce during the COVID-19 pandemic. © Copyright 2022 Bramantoro and Virdyna

关键词： COVID-19

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Evaluation of KNN-NMF Algorithm for Recommendation Systems in E-Commerce 12

Evaluation of KNN-NMF Algorithm for Recommendation Systems i...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Yen, Hew Sok Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Issa, Bayan Yong, Yoke Leng Bashir, Faizah Mohammed Falude, Emmanuel Dodo, Yakubu Aminu Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Petaling Jaya47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria HELP University Persiaran Cakerawala School of Information and Communication Technology Subang Bestari Seksyen Shah AlamU4 40150 Malaysia University of Hail College of Engineering Department of Decoration and Interior Design Engineering Hail55427 Saudi Arabia Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Najran University College of Engineering Architectural Engineering Department Najran66426 Saudi Arabia

ISBN: (纸本)9798350391398

Due to growth in e-commerce, recommender systems have gained importance in providing recommendations to the user to help him or her make a choice. These systems not only assist the users in searching through the extensive product list but also increase businesses' productivity by recommending products. The aim of this project is to suggest a hybrid recommender system which combines the aspects of collaborative and content-based filtering so as to enhance the efficiency of the recommendation system in e-commerce. In addition, it is to evaluate the performance of this model with the individual collaborative and content-based models. The study involved creating the CF and CBF models with the various algorithms like the Matrix Factorization, KNN, SVD, and the TF-IDF vectorization. The best models from CF and CBF were then combined with a Hybrid Filtering (HF) model by using weighted and switching combination to enhance the model's effectiveness. The performance of the CF, CBF and the HF models was compared, in order to check if the proposed hybrid approach is better than the existing models. The results of the experiment show that the hybrid switching model provided better results than the CF and CBF models on their own. The hybrid filtering model had also managed to reduce the RMSE to 1.065 and improved precision to 83.2%. This demonstrates the advantage of the proposed model over the traditional CF and CBF approaches. Our study validates that the hybrid filtering recommender system, especially the switching combination strategy, outperformed the traditional standalone models by integrating the benefits of both collaborative and content-based filtering. This provides a good starting point for future work and there are many directions in which the research can be expanded. © 2024 IEEE.

关键词： Wiener filtering

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A Comparison of Weight Initializers in Deep Learning

A Comparison of Weight Initializers in Deep Learning

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Student Conference on Research and Development (SCOReD)

作者： Akibu Mahmoud Abdullahi Nyi Nyi Nay Naing Md Shakil Hossain Sai Ahkar Htet Shahrinaz Ismail Syaza Lyana Mahadzir School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Technology Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia Centre for Foundation and General Studies Albukhary International University Alor Setar Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Deep learning has revolutionized the field of artificial intelligence and machine learning by enabling the development of complex neural network architectures capable of solving a wide range of real-world problems. Weight initialization in deep learning is a critical phase in the training process that involves assigning initial values to neural network parameters. The challenge at hand is finding the correct balance in initializing weights - not setting them too high or too low - to promote effective training while avoiding complications like disappearing or ballooning gradients. This paper aims to compare the weight initializers in deep learning algorithms provided by Keras Library. The findings indicate that the weight initialization approach used has a considerable impact on the model’s performance. Simpler approaches, such as Zeros and Ones, perform poorly in this case, resulting in slower convergence and less accurate models. Random initializations are superior for learning, with RandomNormal surpassing RandomUniform. Variance and TruncatedNormalScaling approaches show a better combination of exploration and convergence.

关键词： Deep learning Training Accuracy Network topology Neural networks Medical services Libraries

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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The Goofus & Gallant Story Corpus for Practical Value Alignment

arXiv

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arXiv 2025年

作者： Nahian, Md Sultan Al Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States Department of Computer Science University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact. © 2025, CC BY.

关键词： Adversarial machine learning

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Proof-of-Work Difficulty Readjustment with Genetic Algorithm 2nd

Proof-of-Work Difficulty Readjustment with Genetic Algorithm

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2nd International Conference on Advances in Cyber Security, ACeS 2020

作者： Chin, Zi Hau Yap, Timothy Tzen Vun Tan, Ian K. T. Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor63000 Malaysia School of Information Technology Monash University Malaysia Subang Jaya Selangor47500 Malaysia

ISBN: (纸本)9789813368347

Blockchain is a decentralized, distributed and public digital ledger technology. It can be visualized as a gradually increasing list of "blocks" which contains data that are linked together using cryptographic hash. Each transaction is verified by several participating nodes to compute a complex mathematical problem. The complexity of this computation, also known as Proof-of-Work (PoW), is governed by the difficulty set on a periodic basis. If the hash rate of the blockchain’s PoW grows or declines exponentially, the blockchain will be unable to maintain the block creation interval. The utilization of genetic algorithm (GA) in addition with the existing difficulty adjustment algorithm is proposed as a response to this by optimizing the blockchain parameters. A simulation of 3 scenarios as well as the default, were performed and the results were recorded. Based on the results, we are able to observe that the blockchain is able to reach the expected block time 74.4% faster than the blockchain without GA. Moreover, the standard deviations of the average block time and difficulty decreased by 99.4% and 99.5% respectively when block and difficulty intervals were considered for optimization, when compared to the default blockchain without GA. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Oversampling Multi-Label Data Based on Natural Neighbor and Label Correlation

SSRN

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SSRN 2023年

作者： Liu, Bin Zhou, Ao Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing School of Computer Science of Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

In multi-label learning, addressing the class imbalance issue is of paramount importance. Oversampling methods are preferred as they offer a more general solution independent of the model choice, i.e., they alleviate the imbalance of datasets by augmenting instances in the pre-processing step. Existing neighbor-based oversampling methods employ an empirical number of neighbors (k=5), to identify the local region for new instances creation. However, a single fixed k value cannot fit all labels, because every label usually has its own distinct distribution and complexity. Furthermore, the label assignment for synthetic instances usually depends on the statistics of individual labels within the corresponding neighborhood, ignoring the informative correlation among labels. To overcome these limitations, we propose an oversampling method called Multi-Label Oversampling with Natural neighbor and label Correlation (MLONC). Our approach offers three main advantages: 1) the adaptive number of neighbors for each label related to the data complexity is obtained via natural neighbor detection;2) it encourages generating more instances proximate to the decision boundary of highly imbalanced labels, and diminishes the impact of outliers;3) exploitation of label correlation in label assignment enhances the quality of the synthetic instances. Experimental results demonstrate the effectiveness of MLONC under various base classifiers. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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To Improve Supportive Care for Patients Taking Oral Anticancer Agents 18

To Improve Supportive Care for Patients Taking Oral Anticanc...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： He, Lu Song, Ting Jiang, Yun Yu, Ping Song, Lixin Gong, Yang Department of Informatics University of California IrvineCA United States School of Computing and Information Technology University of Wollongong WollongongNSW Australia University of Michigan School of Nursing Ann ArborMI United States School of Nursing & Lineberger Comprehensive Cancer Center University of North Carolina Chapel HillNC United States School of Biomedical Informatics University of Texas Health Science Center 7000 Fannin St HoustonTX77030 United States

ISBN: (纸本)9781643682648

Oral anticancer agents (OAA) are increasingly prescribed to treat cancer because they are flexible and convenient to use. However, managing complex OAA regimens and life-threatening toxicities at home can be challenging for patients and their caregivers. It is urgent to better understand the supportive care needs for OAA and develop novel approaches to facilitating self-management and communicating about OAA. Guided by the chronic care model (CCM), we conducted a grounded theory-based study to analyze OAA-related online discussions and potential mHealth interventions. We found that patients and caregivers commonly used the online community to share personal experiences and concerns, exchange emotional and informational support, identify relevant resources, and obtain benefits of peer coaching. The findings deepen the understanding of the needs for OAA self-management and mHealth interventions, contributing to the development of mHealth models to enhance supportive care and improve communication among peer patients and between patients and providers. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Medical informatics

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High-Rate Quantum Key Distribution Exploiting Integrated Photonics and Multi-Pixel Superconducting Nanowire Detectors

High-Rate Quantum Key Distribution Exploiting Integrated Pho...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Li, Wei Zhang, Likang Lu, Yichen Huang, Jia You, Lixin Mao, Hao-Kun Yan, Bingze Li, Qiong Zhang, Qiang Xu, Feihu Pan, Jian-Wei Hefei National Research Center for Physical Sciences The Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin150080 China

We report a quantum key distribution system that is able to generate key at a record high key rate of 115.8 Mb/s over 10-km fibre. This attributes to a high-efficiency multi-pixel superconducting nanowire detector, a ... 详细信息

ISBN: (纸本)9781957171258

关键词： Quantum cryptography

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Struct2Hair: A hair shape descriptor for hairstyle modeling

Struct2Hair: A hair shape descriptor for hairstyle modeling

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作者： Zhang, Wenshu Nie, Yinyu Guo, Shihui Chang, Jian Zhang, Jianjun Tong, Ruofeng Faculty of Arts and Media Norwich University of the Arts Norfolk United Kingdom The Visual Computing & Artificial Intelligence Lab Technical University of Munich München Germany School of Informatics Xiamen University Fujian China National Centre for Computer Animation Bournemouth University Dorset United Kingdom College of Computer Science and Technology Zhejiang University Zhejiang China

In recent years, it becomes possible to extract hair information for hair reconstruction from multiple cameras or monocular camera. Using a single image as the input avoids the high cost setups and complex calibration compared to multiviewed reconstruction. Taking advantage of an extendible hairstyle database, this paper introduced Struct2Hair, a novel single-viewed hair modelling approach by extracting hair shape descriptor (HSD). The HSD is defined as the fundamental structure-aware feature, which is a combination of critical shapes in a hairstyle. A complete dataset of critical hair shapes is constructed from a known database of three-dimensional (3D) hair models. We first analyze the input two-dimensional (2D) image to extract the orientation information and 2D hair sketch automatically. The extracted information is then used to retrieve the corresponding critical shapes with optimization to build the robust HSD. Finally, the HSD constructs a weighted 3D hair orientation field to guide full-head hair model generation. Our method can preserve local geometric features of hair and retain the whole shape of the hairstyle globally owing to the HSD, which will benefit further hair editing and stylization. © 2022 John Wiley & Sons Ltd.

关键词： Cameras

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